Essays about: "Financial Time Series Forecasting"
Showing result 1 - 5 of 55 essays containing the words Financial Time Series Forecasting.
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1. Artificial Neural Networks for Financial Time Series Prediction
University essay from Stockholms universitet/Institutionen för data- och systemvetenskapAbstract : Financial market forecasting is a challenging and complex task due to the sensitivity of the market to various factors such as political, economic, and social factors. However, recent advances in machine learning and computation technology have led to an increased interest in using deep learning for forecasting financial data. READ MORE
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2. CryptoCurrency Time Series analysis : Comparative analysis between LSTM and BART Algorithm
University essay from Blekinge Tekniska Högskola/Institutionen för datavetenskapAbstract : Background: Cryptocurrency is an innovative digital or virtual form of money thatuses cryptographic techniques for secured financial transactions within a decentralized structure. Due to its high volatility and susceptibility to external factors, itis difficult to understand its behavior which makes accurate predictions challengingfor the investors who are trying to forecast price changes and make profitable investments. READ MORE
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3. Credit Index Forecasting: Stability of an Autoregressive Model
University essay from KTH/Matematik (Avd.)Abstract : This thesis investigates the robustness and stability of total return series for credit bond index investments. Dueto the challenges which arise for financial institutes and investors in achieving these objectives, we aim to createa forecasting model which matches the statistical properties of historical data, while remaining robust, stable andeasy to calibrate. READ MORE
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4. Machine Learning Based Stock Price Prediction by Integrating ARIMA model and Sentiment Analysis with Insights from News and Information
University essay from Blekinge Tekniska Högskola/Institutionen för datavetenskapAbstract : Background: Predicting stock prices in today’s complex financial landscape is asignificant challenge. An innovative approach to address this challenge is integrating sentiment analysis techniques with the well-established Autoregressive IntegratedMoving Average (ARIMA) model. READ MORE
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5. LSTM-based Directional Stock Price Forecasting for Intraday Quantitative Trading
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Deep learning techniques have exhibited remarkable capabilities in capturing nonlinear patterns and dependencies in time series data. Therefore, this study investigates the application of the Long-Short-Term-Memory (LSTM) algorithm for stock price prediction in intraday quantitative trading using Swedish stocks in the OMXS30 index from February 28, 2013, to March 1, 2023. READ MORE